A survey of dynamic graph neural networks

doi:10.1007/s11704-024-3853-2

Front. Comput. Sci.

2025, Vol. 19

Issue (6) : 196323 https://doi.org/10.1007/s11704-024-3853-2

Artificial Intelligence

A survey of dynamic graph neural networks

Yanping ZHENG, Lu YI, Zhewei WEI(

)

Gaoling School of Artificial Intelligence, Renmin University of China, Beijing 100872, China

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Abstract

Graph neural networks (GNNs) have emerged as a powerful tool for effectively mining and learning from graph-structured data, with applications spanning numerous domains. However, most research focuses on static graphs, neglecting the dynamic nature of real-world networks where topologies and attributes evolve over time. By integrating sequence modeling modules into traditional GNN architectures, dynamic GNNs aim to bridge this gap, capturing the inherent temporal dependencies of dynamic graphs for a more authentic depiction of complex networks. This paper provides a comprehensive review of the fundamental concepts, key techniques, and state-of-the-art dynamic GNN models. We present the mainstream dynamic GNN models in detail and categorize models based on how temporal information is incorporated. We also discuss large-scale dynamic GNNs and pre-training techniques. Although dynamic GNNs have shown superior performance, challenges remain in scalability, handling heterogeneous information, and lack of diverse graph datasets. The paper also discusses possible future directions, such as adaptive and memory-enhanced models, inductive learning, and theoretical analysis.

Keywords graph neural networks dynamic graph temporal modeling large-scale

Corresponding Author(s): Zhewei WEI

Just Accepted Date: 24 April 2024 Issue Date: 11 September 2024

Cite this article:

Yanping ZHENG,Lu YI,Zhewei WEI. A survey of dynamic graph neural networks[J]. Front. Comput. Sci., 2025, 19(6): 196323.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-024-3853-2
https://academic.hep.com.cn/fcs/EN/Y2025/V19/I6/196323

Fig.1 The taxonomy of dynamic graph representation learning

Notation	Description
$G$	The graph
$V$	The vertex set
$E$	The edge set
$n$	The number of nodes
$X ∈ R n × d$	The feature matrix
$x i ∈ R d$	The feature vector of node $i$
$A ∈ R n × n$	The adjacent matrix
$D ∈ R n × n$	The degree matrix
$P ∈ R n × n$	The normalized adjacent matrix
$L ∈ R n × n$	The Laplacian matrix
$σ$	The nonlinear activation function
$G$	The dynamic graph

Tab.1 Notations and the corresponding definitions.

Fig.2 Comparative illustration of different graph representations. (a) Representation of a static graph where the structure remains unchanged over time, without temporal information; (b) Illustration of a continuous-time dynamic graph (CTDG) where interactions between nodes are labeled by timestamps, and multiple interactions are allowed; (c) Representation of a discrete-time dynamic graph (DTDG), which captures the evolution of relationships in discrete time intervals

Method	Models	Graph	Insertion		Deletion		Attribute
Method	Models	Graph	Node	Edge	Node	Edge	Node	Edge
WD-GCN [36]	GCN with skip connections and LSTM	DTDG	×	√	×	√	√	√
DySAT [37]	GAT and Transformer	DTDG	√	√	√	√	√	√
TNDCN [38]	GNNs and spatio-temporal convolutions	DTDG	×	√	×	√	√	√
TEDIC [39]	GNNs and spatio-temporal convolutions	DTDG	×	√	×	√	√	√
GC-LSTM [40]	GCN and LSTM	DTDG	×	√	×	√	√	√
LRGCN [41]	R-GCN and LSTM	DTDG	×	√	×	√	√	√
RE-Net [42]	integrate R-GCN within RNNs	DTDG	×	√	×	√	×	×
WinGNN [43]	GNN and randomized sliding-window	DTDG	√	√	√	√	□^*	×
ROLAND [4]	GNNs and RNNs	DTDG	√	√	√	√	√	√
EvolveGCN [44]	GCN and LSTM/GRU	DTDG	√	√	√	√	√	√
SEIGN [45]	GCN and LSTM/GRU	DTDG	√	√	√	√	√	√
dyngraph2vec [46]	AE and LSTM	DTDG	√	√	√	√	×	×
Know-Evolve [47]	RNN-parameterized TPP	CTDG	×	√	×	×	×	×
DyREP [48]	RNN-parameterized TPP	CTDG	√	√	×	×	×	×
LDG [49]	RNN-parameterized TPP and self attention	CTDG	√	√	×	×	×	×
GHNN [50]	LSTM-parameterized TPP	CTDG	×	√	×	×	×	×
GHT [51]	LSTM-parameterized TPP	CTDG	×	√	×	×	×	×
TREND [52]	Hawkes process-based GNN	CTDG	√	√	×	×	□^*	×
DGNN [53]	GNNs and LSTM	CTDG	√	√	×	×	×	×
JODIE [54]	GNNs and RNNs	CTDG	√	√	×	×	□^*	√
TGAT [2]	GAT, time encode and RNNs	CTDG	√	√	×	×	□^*	√
TGN [3]	GNNs, time encode and RNNs	CTDG	√	√	×	×	□^*	√
APAN [55]	GNNs and RNNs	CTDG	√	√	×	×	□^*	√
CAW-N [56]	causal anonymous walk based GNN	CTDG	√	√	×	×	×	√
Zebra [57]	temporal PPR based GNN	CTDG	√	√	×	×	√	√
EARLY [58]	GCN and temporal random walk	CTDG	√	√	√	√	√	√
Zheng et al. [59]	decoupled GNNs and sequential models	CTDG	√	√	√	√	√	√
SDG [60]	APPNP and dynamic propagation	CTDG	×	√	×	√	√	√

Tab.2 Comparison of graph neural network methods in literature review. √ represents that the method supports the corresponding graph event, × means it does not support that event, and □^* denotes that it considers only static attributes

Fig.3 Different model architectures for dynamic graphs. (a) Stacked dynamic GNN for DTDGs; (b) integrated dynamic GNN for DTDGs; (c) dynamic GNN for CTDGs

Tab.3 Scalable dynamic GNNs training framework

Types	Dataset	Domain	Category	$n$	$m$	$\| T \|$	$d e$
CTDG	Social Evolution [79]	Proximity	General	74	2,099,519	565,932	1^?
	Enron [80]	Social	General	184	125,235	22,632	?
	Contact [81]	Proximity	General	692	2,426,279	8,065	1^?
	UCI [82]	Social	General	1,899	59,835	58,911	100
	LastFM [54]	Interaction	Bipartite	1,980	1,293,103	1,283,614	?
	Bitcoin-Alpha [83,84]	Finance	General	3,783	24,186	24,186	?
	Bitcoin-OTC [83,84]	Finance	General	5,881	35,592	35,592	?
	MOOC [54]	Interaction	Bipartite	7,144	411,749	345,600	4^?
	Wikipedia [54]	Social	Bipartite	9,227	157,474	152,757	172
	Reddit [54]	Social	Bipartite	10,984	672,447	669,065	172
	GDELT [74,85]	Event	General	16,682	191,290,882	170,522	186
	eBay-Small [86]	E-commerce	Bipartite	38,427	384,677	?	?
	Taobao [87,88]	E-commerce	Bipartite	82,566	77,436	?	4^?
	YouTube-Reddit-Small [89]	Social	Bipartite	264,443	297,732	?	?
	eBay-Large [86]	E-commerce	Bipartite	1,333,594	1,119,454	?	?
	Taobao-Large [87,88]	E-commerce	Bipartite	1,630,453	5,008,745	?	4^?
	DGraphFin [90]	Social	General	3,700,550	4,300,999	?	?
	Youtube-Reddit-Large [89]	Social	Bipartite	5,724,111	4,228,523	?	?
DTDG	UN Vote [91]	Politics	General	201	1,035,742	72	1^?
	US Legislative [92,93]	Politics	General	225	60,396	12	1^?
	UN Trade [94]	Finance	General	255	507,497	32	1^?
	Canadian Parliament [92]	Politics	General	734	74,478	14	?
	Twitter-Tennis [95]	Social	General	1,000	40,839	120	?
	Autonomous systems [96]	Communication	General	7,716	13,895	733	?
	Flights [97]	Transport	General	13,169	1,927,145	122	1^?
	HEP-TH [96,98]	Citation	General	27,770	352,807	3,487	?
	Elliptic [99]	Finance	General	203,769	234,355	49	?
	MAG [11,74]	Citation	General	121,751,665	1,297,748,926	120	?

Tab.4 The statistics of datasets, where

n

represents the number of nodes,

m

denotes the number of edges,

| T |

indicates the number of timestamps or snapshots, and

d e

refers to the dimension of edge features

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